An action-oriented perspective changes the role of an individual from a passive observer to an actively engaged agent interacting in a closed loop with the world as well as with others. Cognition exists to serve action within a landscape that contains both. This chapter surveys this landscape and addresses the status of the pragmatic turn. Its potential influence on science and the study of cognition are considered (including perception, social cognition, social interaction, sensorimotor entrainment, and language acquisition) and its impact on how neuroscience is studied is also investigated (with the notion that brains do not passively build models, but instead support the guidance of action).
A review of its implications in robotics and engineering includes a discussion of the application of enactive control principles to couple action and perception in robotics as well as the conceptualization of system design in a more holistic, less modular manner. Practical applications that can impact the human condition are reviewed (e.g. educational applications, treatment possibilities for developmental and psychopathological disorders, the development of neural prostheses). All of this foreshadows the potential societal implications of the pragmatic turn. The chapter concludes that an action-oriented approach emphasizes a continuum of interaction between technical aspects of cognitive systems and robotics, biology, psychology, the social sciences, and the humanities, where the individual is part of a grounded cultural system.

Since the 1950s, robotics research has sought to build a general-purpose agent capable of autonomous, open-ended interaction with realistic, unconstrained environments. Cognition is perceived to be at the core of this process, yet understanding has been challenged because cognition is referred to differently within and across research areas, and is not clearly defined. The classic robotics approach is decomposition into functional modules which perform planning, reasoning, and problem-solving or provide input to these mechanisms. Although advancements have been made and numerous success stories reported in specific niches, this systems-engineering approach has not succeeded in building such a cognitive agent.
The emergence of an action-oriented paradigm offers a new approach: action and perception are no longer separable into functional modules but must be considered in a complete loop. This chapter reviews work on different mechanisms for action- perception learning and discusses the role of embodiment in the design of the underlying representations and learning. It discusses the evaluation of agents and suggests the development of a new embodied Turing Test. Appropriate scenarios need to be devised in addition to current competitions, so that abilities can be tested over long time periods.

We develop means of learning and representing object grasp affordances probabilistically. By grasp affordance, we refer to an entity that is able to assess whether a given relative object-gripper configuration will yield a stable grasp. These affordances are represented with grasp densities, continuous probability density functions defined on the space of 3D positions and orientations. Grasp densities are registered with a visual model of the object they characterize. They are exploited by aligning them to a target object using visual pose estimation. Grasp densities are refined through experience: A robot “plays” with an object by executing grasps drawn randomly for the object’s grasp density. The robot then uses the outcomes of these grasps to build a richer density through an importance sampling mechanism. Initial grasp densities, called hypothesis densities, are bootstrapped from grasps collected using a motion capture system, or from grasps generated from the visual model of the object. Refined densities, called empirical densities, represent affordances that have been confirmed through physical experience. The applicability of our method is demonstrated by producing empirical densities for two object with a real robot and its 3-finger hand. Hypothesis densities are created from visual cues and human demonstration.

Traditional motor primitive approaches deal largely with open-loop policies which can only deal with small perturbations. In this paper, we present a new type of motor primitive policies which serve as closed-loop policies together with an appropriate learning algorithm. Our new motor primitives are an augmented version version of the dynamical system-based motor primitives [Ijspeert et al(2002)Ijspeert, Nakanishi, and Schaal] that incorporates perceptual coupling to external variables. We show that these motor primitives can perform complex tasks such as Ball-in-a-Cup or Kendama task even with large variances in the initial conditions where a skilled human player would be challenged. We initialize the open-loop policies by imitation learning and the perceptual coupling with a handcrafted solution. We first improve the open-loop policies and subsequently the perceptual coupling using a novel reinforcement learning method which is particularly well-suited for dynamical system-based motor primitives.

The number of advanced robot systems has been increasing in recent years yielding a large variety of versatile designs with many degrees of freedom. These robots have the potential of being applicable in uncertain tasks outside wellstructured industrial settings. However, the complexity of both systems and tasks is often beyond the reach of classical robot programming methods. As a result, a more autonomous solution for robot task acquisition is needed where robots adaptively adjust their behaviour to the encountered situations and required tasks.
Learning approaches pose one of the most appealing ways to achieve this goal. However, while learning approaches are of high importance for robotics, we cannot simply use off-the-shelf methods from the machine learning community as these usually do not scale into the domains of robotics due to excessive computational cost as well as a lack of scalability. Instead, domain appropriate approaches are needed. In this book, we focus on several core domains of robot learning. For accurate task execution, we need motor learning capabilities. For fast learning of the motor tasks, imitation learning offers the most promising approach. Self improvement requires reinforcement learning approaches that scale into the domain of complex robots. Finally, for efficient interaction of humans with robot systems, we will need a form of interaction learning. This chapter provides a general introduction to these issues and briefly presents the contributions of the subsequent chapters to the corresponding research topics.

For many applications in robotics, accurate dynamics models are essential. However, in some applications, e.g., in model-based tracking control, precise dynamics models cannot be obtained analytically for sufficiently complex robot systems. In such cases, machine learning offers a promising alternative for approximating the robot dynamics using measured data. However, standard regression methods such as Gaussian process regression (GPR) suffer from high computational complexity which prevents their usage for large numbers of samples or online learning to date. In this paper, we propose an approximation to the standard GPR using local Gaussian processes models inspired by [Vijayakumar et al(2005)Vijayakumar, DSouza, and Schaal, Snelson and Ghahramani(2007)]. Due to reduced computational cost, local Gaussian processes (LGP) can be applied for larger sample-sizes and online learning. Comparisons with other nonparametric regressions, e.g., standard GPR, support vector regression (SVR) and locally weighted proje
ction regression (LWPR), show that LGP has high approximation accuracy while being sufficiently fast for real-time online learning.

We aim to color greyscale images automatically, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is that we deal directly with multimodality and estimate, for each pixel of the image to be colored, the probability distribution of all possible colors, instead of choosing the most probable color at the local level. We also predict the expected variation of color at each pixel, thus defining a non-uniform spatial coherency criterion. We then use graph cuts to maximize the probability of the whole colored image at the global level. We work in the L-a-b color space in order to approximate the human perception of distances between colors, and we use machine learning tools to extract as much information as possible from a dataset of colored examples. The resulting algorithm is fast, designed to be more robust to texture noise, and is above all able to deal with ambiguity, in contrary to previous approaches.

This chapter presents an extensive and critical review on the use of kernel methods and in particular of support vector machines (SVMs) in the classification of remote-sensing (RS) data. The chapter recalls the mathematical formulation and the main theoretical concepts related to SVMs, and discusses the motivations at the basis of the use of SVMs in remote sensing. A review on the main applications of SVMs in classification of remote sensing is given, presenting a literature survey on the use of SVMs for the analysis of different kinds of RS images. In addition, the most recent methodological developments related to SVM-based classification techniques in RS are illustrated by focusing on semisupervised, domain adaptation, and context sensitive approaches. Finally, the most promising research directions on SVM in RS are identified and discussed.

Recent approaches to independent component analysis have used kernel
independence measures to obtain very good performance in ICA, particularly
in areas where classical methods experience difficulty (for instance,
sources with near-zero kurtosis). In this chapter, we compare two efficient
extensions of these methods for large-scale problems: random subsampling
of entries in the Gram matrices used in defining the independence
measures, and incomplete Cholesky decomposition of these matrices.
We derive closed-form, efficiently computable approximations for the
gradients of these measures, and compare their performance on ICA using
both artificial and music data. We show that kernel ICA can scale up to much larger
problems than yet attempted, and that incomplete Cholesky decomposition
performs better than random sampling.

Most literature on Support Vector Machines (SVMs) concentrate on
the dual optimization problem. In this paper, we would like to point out
that the primal problem can also be solved efficiently, both for linear
and non-linear SVMs, and that there is no reason to ignore this possibility.
On the contrary, from the primal point of view new families of algorithms for
large scale SVM training can be investigated.

A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.

Convex learning algorithms, such as Support Vector Machines (SVMs), are often
seen as highly desirable because they offer strong practical properties and are
amenable to theoretical analysis. However, in this work we show how nonconvexity
can provide scalability advantages over convexity. We show how concave-convex
programming can be applied to produce (i) faster SVMs where training errors are
no longer support vectors, and (ii) much faster Transductive SVMs.

In this chapter we are concerned with the problem of reconstructing patterns from their representation in feature space, known as the pre-image problem. We review existing algorithms and propose a learning based approach. All algorithms are discussed regarding their usability and complexity and evaluated on an image denoising application.

In the past, computational motor control has been approached from at least two major frameworks: the dynamic systems approach and the viewpoint of optimal control. The dynamic system approach emphasizes motor control as a process of self-organization between an animal and its environment. Nonlinear differential equations that can model entrainment and synchronization behavior are among the most favorable tools of dynamic systems modelers. In contrast, optimal control approaches view motor control as the evolutionary or development result of a nervous system that tries to optimize rather general organizational principles, e.g., energy consumption or accurate task achievement. Optimal control theory is usually employed to develop appropriate theories. Interestingly, there is rather little interaction between dynamic systems and optimal control modelers as the two approaches follow rather different philosophies and are often viewed as diametrically opposing. In this paper, we develop a computational approach to motor control that offers a unifying modeling framework for both dynamic systems and optimal control approaches. In discussions of several behavioral experiments and some theoretical and robotics studies, we demonstrate how our computational ideas allow both the representation of self-organizing processes and the optimization of movement based on reward criteria. Our modeling framework is rather simple and general, and opens opportunities to revisit many previous modeling results from this novel unifying view.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems